Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Abstract

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three OPEN ACCESSRemote Sens. 2010, 2 1703 explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process.

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

“…Since vegetation structure and composition of most arid regions is rather simple, the identification of vegetation objects using high spatial resolution remote sensing has been carried out successfully [3][4][5]. Nevertheless, the estimation of biophysical properties such as canopy water content, chlorophyll content or leaf area index for small vegetation objects has not been sufficiently studied yet due to the limited spectral information of VHSR satellites [6,7]. These sensors typically have only three bands in the visible region and one band in the near infra-red region (IKONOS, QuickBird2 and Geo-Eye).…”

Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

“…Multispectral (several bands) and hyperspectral (hundreds of narrow bands) remote sensing has been used for monitoring hazardous sites [3,[9][10][11] as well as typical environmental resources such as water, land, and vegetation [12][13][14][15]. In particular, remote sensing-derived vegetation products can provide valuable information regarding vegetation health and dynamics when monitoring hazardous waste sites [16].…”

Measuring and modeling water-related soil–vegetation feedbacks in a fallow plot

“…Furthermore, the vegetation greenness is evaluated as a function of the average normalized red (r i ), green (g i ) and blue (b i ). Borzuchowski and Schulz (2010) revise a list of vegetation spectral indices to describe plant eco-physiological parameters. None of them can be estimated in the visible spectrum, due to its restricted range of reflectance.…”